""" WiderFace evaluation code author: wondervictor mail: tianhengcheng@gmail.com copyright@wondervictor """ import os import tqdm import pickle import argparse import numpy as np from scipy.io import loadmat from bbox import bbox_overlaps from IPython import embed def get_gt_boxes(gt_dir): """ gt dir: (wider_face_val.mat, wider_easy_val.mat, wider_medium_val.mat, wider_hard_val.mat)""" gt_mat = loadmat(os.path.join(gt_dir, 'wider_face_val.mat')) hard_mat = loadmat(os.path.join(gt_dir, 'wider_hard_val.mat')) medium_mat = loadmat(os.path.join(gt_dir, 'wider_medium_val.mat')) easy_mat = loadmat(os.path.join(gt_dir, 'wider_easy_val.mat')) facebox_list = gt_mat['face_bbx_list'] event_list = gt_mat['event_list'] file_list = gt_mat['file_list'] hard_gt_list = hard_mat['gt_list'] medium_gt_list = medium_mat['gt_list'] easy_gt_list = easy_mat['gt_list'] return facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list def get_gt_boxes_from_txt(gt_path, cache_dir): cache_file = os.path.join(cache_dir, 'gt_cache.pkl') if os.path.exists(cache_file): f = open(cache_file, 'rb') boxes = pickle.load(f) f.close() return boxes f = open(gt_path, 'r') state = 0 lines = f.readlines() lines = list(map(lambda x: x.rstrip('\r\n'), lines)) boxes = {} print(len(lines)) f.close() current_boxes = [] current_name = None for line in lines: if state == 0 and '--' in line: state = 1 current_name = line continue if state == 1: state = 2 continue if state == 2 and '--' in line: state = 1 boxes[current_name] = np.array(current_boxes).astype('float32') current_name = line current_boxes = [] continue if state == 2: box = [float(x) for x in line.split(' ')[:4]] current_boxes.append(box) continue f = open(cache_file, 'wb') pickle.dump(boxes, f) f.close() return boxes def read_pred_file(filepath): with open(filepath, 'r') as f: lines = f.readlines() img_file = lines[0].rstrip('\n\r') lines = lines[2:] # b = lines[0].rstrip('\r\n').split(' ')[:-1] # c = float(b) # a = map(lambda x: [[float(a[0]), float(a[1]), float(a[2]), float(a[3]), float(a[4])] for a in x.rstrip('\r\n').split(' ')], lines) boxes = [] for line in lines: line = line.rstrip('\r\n').split(' ') if line[0] is '': continue # a = float(line[4]) boxes.append([float(line[0]), float(line[1]), float(line[2]), float(line[3]), float(line[4])]) boxes = np.array(boxes) # boxes = np.array(list(map(lambda x: [float(a) for a in x.rstrip('\r\n').split(' ')], lines))).astype('float') return img_file.split('/')[-1], boxes def get_preds(pred_dir): events = os.listdir(pred_dir) boxes = dict() pbar = tqdm.tqdm(events) for event in pbar: pbar.set_description('Reading Predictions ') event_dir = os.path.join(pred_dir, event) event_images = os.listdir(event_dir) current_event = dict() for imgtxt in event_images: imgname, _boxes = read_pred_file(os.path.join(event_dir, imgtxt)) current_event[imgname.rstrip('.jpg')] = _boxes boxes[event] = current_event return boxes def norm_score(pred): """ norm score pred {key: [[x1,y1,x2,y2,s]]} """ max_score = 0 min_score = 1 for _, k in pred.items(): for _, v in k.items(): if len(v) == 0: continue _min = np.min(v[:, -1]) _max = np.max(v[:, -1]) max_score = max(_max, max_score) min_score = min(_min, min_score) diff = max_score - min_score for _, k in pred.items(): for _, v in k.items(): if len(v) == 0: continue v[:, -1] = (v[:, -1] - min_score)/diff def image_eval(pred, gt, ignore, iou_thresh): """ single image evaluation pred: Nx5 gt: Nx4 ignore: """ _pred = pred.copy() _gt = gt.copy() pred_recall = np.zeros(_pred.shape[0]) recall_list = np.zeros(_gt.shape[0]) proposal_list = np.ones(_pred.shape[0]) _pred[:, 2] = _pred[:, 2] + _pred[:, 0] _pred[:, 3] = _pred[:, 3] + _pred[:, 1] _gt[:, 2] = _gt[:, 2] + _gt[:, 0] _gt[:, 3] = _gt[:, 3] + _gt[:, 1] overlaps = bbox_overlaps(_pred[:, :4], _gt) for h in range(_pred.shape[0]): gt_overlap = overlaps[h] max_overlap, max_idx = gt_overlap.max(), gt_overlap.argmax() if max_overlap >= iou_thresh: if ignore[max_idx] == 0: recall_list[max_idx] = -1 proposal_list[h] = -1 elif recall_list[max_idx] == 0: recall_list[max_idx] = 1 r_keep_index = np.where(recall_list == 1)[0] pred_recall[h] = len(r_keep_index) return pred_recall, proposal_list def img_pr_info(thresh_num, pred_info, proposal_list, pred_recall): pr_info = np.zeros((thresh_num, 2)).astype('float') for t in range(thresh_num): thresh = 1 - (t+1)/thresh_num r_index = np.where(pred_info[:, 4] >= thresh)[0] if len(r_index) == 0: pr_info[t, 0] = 0 pr_info[t, 1] = 0 else: r_index = r_index[-1] p_index = np.where(proposal_list[:r_index+1] == 1)[0] pr_info[t, 0] = len(p_index) pr_info[t, 1] = pred_recall[r_index] return pr_info def dataset_pr_info(thresh_num, pr_curve, count_face): _pr_curve = np.zeros((thresh_num, 2)) for i in range(thresh_num): _pr_curve[i, 0] = pr_curve[i, 1] / pr_curve[i, 0] _pr_curve[i, 1] = pr_curve[i, 1] / count_face return _pr_curve def voc_ap(rec, prec): # correct AP calculation # first append sentinel values at the end mrec = np.concatenate(([0.], rec, [1.])) mpre = np.concatenate(([0.], prec, [0.])) # compute the precision envelope for i in range(mpre.size - 1, 0, -1): mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i]) # to calculate area under PR curve, look for points # where X axis (recall) changes value i = np.where(mrec[1:] != mrec[:-1])[0] # and sum (\Delta recall) * prec ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) return ap def evaluation(pred, gt_path, iou_thresh=0.5): pred = get_preds(pred) norm_score(pred) facebox_list, event_list, file_list, hard_gt_list, medium_gt_list, easy_gt_list = get_gt_boxes(gt_path) event_num = len(event_list) thresh_num = 1000 settings = ['easy', 'medium', 'hard'] setting_gts = [easy_gt_list, medium_gt_list, hard_gt_list] aps = [] for setting_id in range(3): # different setting gt_list = setting_gts[setting_id] count_face = 0 pr_curve = np.zeros((thresh_num, 2)).astype('float') # [hard, medium, easy] pbar = tqdm.tqdm(range(event_num)) for i in pbar: pbar.set_description('Processing {}'.format(settings[setting_id])) event_name = str(event_list[i][0][0]) img_list = file_list[i][0] pred_list = pred[event_name] sub_gt_list = gt_list[i][0] # img_pr_info_list = np.zeros((len(img_list), thresh_num, 2)) gt_bbx_list = facebox_list[i][0] for j in range(len(img_list)): pred_info = pred_list[str(img_list[j][0][0])] gt_boxes = gt_bbx_list[j][0].astype('float') keep_index = sub_gt_list[j][0] count_face += len(keep_index) if len(gt_boxes) == 0 or len(pred_info) == 0: continue ignore = np.zeros(gt_boxes.shape[0]) if len(keep_index) != 0: ignore[keep_index-1] = 1 pred_recall, proposal_list = image_eval(pred_info, gt_boxes, ignore, iou_thresh) _img_pr_info = img_pr_info(thresh_num, pred_info, proposal_list, pred_recall) pr_curve += _img_pr_info pr_curve = dataset_pr_info(thresh_num, pr_curve, count_face) propose = pr_curve[:, 0] recall = pr_curve[:, 1] ap = voc_ap(recall, propose) aps.append(ap) print("==================== Results ====================") print("Easy Val AP: {}".format(aps[0])) print("Medium Val AP: {}".format(aps[1])) print("Hard Val AP: {}".format(aps[2])) print("=================================================") if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-p', '--pred', default="./widerface_txt/") parser.add_argument('-g', '--gt', default='./ground_truth/') args = parser.parse_args() evaluation(args.pred, args.gt)